| Citation: |
QIAO Zhiping, HUANG Jingying, WANG Lihe. Infrared Dual-band Target Detecting Fusion Algorithm Based on Multiple Features[J]. Infrared Technology , 2024, 46(10): 1201-1208.
|
Infrared target detection algorithms play important roles in the military and civilian fields and have been widely studied. However, relatively few studies have been conducted on the use of dual-band images for targeted detection. To fully utilize the advantages of dual-band images in target detection, a fusion algorithm based on multiple features of infrared dual-band images was proposed through an in-depth analysis of the detection results. The proposed fusion algorithm utilizes a deep learning-based multi-feature fusion network to process the detection results of dual-band images, fully mine the feature information of the target, adaptively select the detection results of a single band as the output, and obtain the final decision-level fusion detection results. The experimental results show that, compared with using single-band images for object detection, the proposed infrared dual-band fusion algorithm based on multiple features can effectively utilize information from different bands, improve the detection performance, and fully leverage the advantages of infrared object detection equipment.
| [1] |
吴双忱, 左峥嵘. 基于深度卷积神经网络的红外小目标检测[J]. 红外与毫米波学报, 2019, 38(3): 371-380.
WU Shuangchen, ZUO Zhengrong. Small target detection in infrared images using deep convolutional neural networks[J]. Journal of Infrared and Millimeter Waves, 2019, 38(3): 371-380.
|
| [2] |
汪洋, 郑亲波, 张钧屏. 基于数学形态学的红外图像小目标检测[J]. 红外与激光工程, 2003, 32(1): 28-31.
WANG Yang, ZHENG Qinbo, ZHANG Junping. Real-time detection of small target in IR grey image based on mathematical morphology[J]. Infrared and Laser Engineering, 2003, 32(1): 28-31.
|
| [3] |
张毅刚, 曹阳, 项学智. 基于形态学Top-hat滤波的红外小目标检测[J]. 计算机测量与控制, 2011, 19(6): 1269-1272.
ZHANG Yigang, CAO Yang, XIANG Xuezhi. Infrared small target detection based on morphological top-hat filtering[J]. Computer Measurement & Control, 2011, 19(6): 1269-1272.
|
| [4] |
熊辉, 沈振康, 魏急波, 等. 低信噪比运动红外点目标的检测[J]. 电子学报, 1999(12): 26-29. DOI: 10.3321/j.issn:0372-2112.1999.12.008
XIONG Hui, SHEN Zhenkang, WEI Jibo, et al. Moving infrared low SNR target detection algorithm[J]. Chinese Journal of Electronics, 1999(12): 26-29. DOI: 10.3321/j.issn:0372-2112.1999.12.008
|
| [5] |
熊艳, 彭嘉雄, 丁明跃, 等. 基于线性变系数差分方程的运动目标检测方法[J]. 自动化学报, 1996, 22(4): 485-488.
XIONG Yan, PENG Jiaxiong, DING Mingyue, et al. A method based on linear-variant-coefficient-difference-equation for moving target identi-fication[J]. Acta Automatica Sinica, 1996, 22(4): 485-488.
|
| [6] |
王春歆, 张玉叶, 王学伟, 等. 空间小目标动态规划检测[J]. 光学精密工程, 2010, 18(2): 477-484.
WANG Chunxin, ZHANG Yuye, WANG Xuewei, et al. Study on dynamic programming algorithm for space small targets detection[J]. Optics and Precision Engineering, 2010, 18(2): 477-484.
|
| [7] |
ZHAO B, WANG C, FU Q, et al. A novel pattern for infrared small target detection with generative adversarial network[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(5): 4481-4492.
|
| [8] |
DAI Y, WU Y, ZHOU F, et al. Attentional local contrast networks for infrared small target detection[C]//IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(11): 9813-9824.
|
| [9] |
白玉, 侯志强, 刘晓义, 等. 基于可见光图像和红外图像决策级融合的目标检测算法[J]. 空军工程大学学报, 2020, 21(6): 53-59.
BAI Yu, HOU Zhiqiang, LIU Xiaoyi, et al. An object detection algorithm based on decision level fusion of visible light image and infrared image[J]. Journal of Air Force Engineering University, 2020, 21(6): 53-59.
|
Supported by: Beijing Renhe Information Technology Co., Ltd. 
DownLoad: